• views: 155
• date: 19 November 2022
• posted by: mitsumi

What you'll learn
Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in practice
Understand the intuition behind Recurrent Neural Networks
Understand the intuition behind Convolution Neural Networks
Learn how to apply neural networks in several practical examples
Build model in tensorflow and keras
Requirements
Basic understanding about Python: variables, functions, OOP
A Google account (google-colab is used as the Python IDE)
Description
Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.Deep Learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.Deep Learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Before you start!!
Lecture 3 Setting up Google Colab
Section 2: Building Theoretical Concept
Lecture 4 Neurons Explained!
Lecture 5 Introduction to neural network
Lecture 6 Introduction to Activation Function
Section 3: Building Practical Concept
Lecture 7 Introduction to tensor
Lecture 8 Real World Tensor Example
Lecture 9 Tensor Operations
Lecture 10 Introduction to Gradient Optimization
Lecture 11 Gradient Optimization in detail
Lecture 12 BackPropagation
Lecture 13 Example of Neural Network
Section 4: Getting started with Neural Network
Lecture 14 Introduction to the section
Lecture 15 Anatomy of Neural Network
Lecture 16 Quick Overview of Keras
Lecture 17 Movie Review Classification Problem
Lecture 18 Classifying NewsWire
Lecture 19 Predicting House Price
Students who wants to learn about Deep Learning,Machine-learning enthusiasts,Data scientists who want to expand their library of skills,Scientists and researchers interested in deep learning



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